OpenRaiser/Intern-Atlas huggingface dataset:
Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .
Overlapping Community Detection by Online Cluster Aggregation
Venue: arXiv cs.LG 2015
Year: 2015

We present a new online algorithm for detecting overlapping communities. The main ingredients are a modification of an online k-means algorithm and a new approach to modelling overlap in communities. An evaluation on large benchmark graphs shows that the quality of discovered communities compares favorably to several methods in the recent literature, while the running time is significantly improved.
Comparison of Training Methods for Deep Neural Networks
Venue: arXiv cs.LG 2015
Year: 2015

This report describes the difficulties of training neural networks and in particular deep neural networks. It then provides a literature review of training methods for deep neural networks, with a focus on pre-training. It focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and Stacked Autoencoders and provides an outreach on further and alternative approaches. It also includes related practical recommendations from the literature on training them. In the second part, initial experiments using some of the covered methods are performed on two databases. In particular, experiments are performed on the MNIST hand-written digit dataset and on facial emotion data from a Kaggle competition. The results are discussed in the context of results reported in other research papers. An error rate lower than the best contribution to the Kaggle competition is achieved using an optimized Stacked Autoencoder.
Two Trends in Mobile Security: Financial Motives and Transitioning from Static to Dynamic Analysis
Venue: arXiv cs.CR 2015
Year: 2015

The goal of this paper is to analyze the behavior and intent of recent types of privacy invasive Android adware. There are two recent trends in this area: more financial motives instead of ego motives, and the development of more dynamic analysis tools. This paper starts with a review of Android mobile operating system security, and also addresses the pros and cons of open source operating system security. Static analysis of malware provides high quality results and leads to a good understanding as shown in this paper. However, as malware grows in number and complexity, there have been recent efforts to automate the detection mechanisms and many of the static tasks. As Android's market share is rapidly growing around the world. Android security will be a crucial area of research for IT security professionals and their academic counterparts. The upside of the current situation is that malware is being quickly exposed, thanks to open source software development tools. This cooperation is important in curbing the widespread theft of personal information with monetary value.
Full-duplex MAC Protocol Design and Analysis
Venue: arXiv cs.NI 2015
Year: 2015

The idea of in-band full-duplex (FD) communications revives in recent years owing to the significant progress in the self-interference cancellation and hardware design techniques, offering the potential to double spectral efficiency. The adaptations in upper layers are highly demanded in the design of FD communication systems. In this letter, we propose a novel medium access control (MAC) using FD techniques that allows transmitters to monitor the channel usage while transmitting, and backoff as soon as collision happens. Analytical saturation throughput of the FD-MAC protocol is derived with the consideration of imperfect sensing brought by residual self- interference (RSI) in the PHY layer. Both analytical and simulation results indicate that the normalized saturation throughput of the proposed FD-MAC can significantly outperforms conventional CSMA/CA under various network conditions.
Making sense of Open Data Statistics with Information from Wikipedia
Venue: arXiv cs.HC 2015
Year: 2015

Today, more and more open data statistics are published by governments, statistical offices and organizations like the United Nations, The World Bank or Eurostat. This data is freely available and can be consumed by end users in interactive visualizations. However, additional information is needed to enable laymen to interpret these statistics in order to make sense of the raw data. In this paper, we present an approach to combine open data statistics with historical events. In a user interface we have integrated interactive visualizations of open data statistics with a timeline of thematically appropriate historical events from Wikipedia. This can help users to explore statistical data in several views and to get related events for certain trends in the timeline. Events include links to Wikipedia articles, where details can be found and the search process can be continued. We have conducted a user study to evaluate if users can use the interface intuitively, if relations between trends in statistics and historical events can be found and if users like this approach for their exploration process.
The fractional Fisher information and the central limit theorem for stable laws
Venue: arXiv cs.IT 2015
Year: 2015

A new information-theoretic approach to the central limit theorem for stable laws is presented. The main novelty is the concept of relative fractional Fisher information, which shares most of the properties of the classical one, included Blachman-Stam type inequalities. These inequalities relate the fractional Fisher information of the sum of $n$ independent random variables to the information contained in sums over subsets containing $n-1$ of the random variables. As a consequence, a simple proof of the monotonicity of the relative fractional Fisher information in central limit theorems for stable law is obtained, together with an explicit decay rate.
Secure and Verifiable Electronic Voting in Practice: the use of vVote in the Victorian State Election
Venue: arXiv cs.CR 2015
Year: 2015

The November 2014 Australian State of Victoria election was the first statutory political election worldwide at State level which deployed an end-to-end verifiable electronic voting system in polling places. This was the first time blind voters have been able to cast a fully secret ballot in a verifiable way, and the first time a verifiable voting system has been used to collect remote votes in a political election. The code is open source, and the output from the election is verifiable. The system took 1121 votes from these particular groups, an increase on 2010 and with fewer polling places.
Physical Layer Security for Massive MIMO: An Overview on Passive Eavesdropping and Active Attacks
Venue: arXiv cs.IT 2015
Year: 2015

This article discusses opportunities and challenges of physical layer security integration in massive multiple-input multiple-output (MaMIMO) systems. Specifically, we first show that MaMIMO itself is robust against passive eavesdropping attacks. We then review a pilot contamination scheme which actively attacks the channel estimation process. This pilot contamination attack is not only dramatically reducing the achievable secrecy capacity but is also difficult to detect. We proceed by reviewing some methods from literature that detect active attacks on MaMIMO. The last part of the paper surveys the open research problems that we believe are the most important to address in the future and give a few promising directions of research to solve them.
Location-aware sign-on and key exchange using attribute-based encryption and Bluetooth beacons
Venue: arXiv cs.CR 2015
Year: 2015

This work presents a mobile sign-on scheme, which utilizes Bluetooth Low Energy beacons for location awareness and Attribute-Based Encryption for expressive, broadcast-style key exchange. Bluetooth Low Energy beacons broadcast encrypted messages with encoded access policies. Within range of the beacons, a user with appropriate attributes is able to decrypt the broadcast message and obtain parameters that allow the user to perform a short or simplified login. The effect is a "traveling" sign-on that accompanies the user throughout different locations.
Communication Strategies for Low-Latency Trading
Venue: arXiv cs.IT 2015
Year: 2015

The possibility of latency arbitrage in financial markets has led to the deployment of high-speed communication links between distant financial centers. These links are noisy and so there is a need for coding. In this paper, we develop a gametheoretic model of trading behavior where two traders compete to capture latency arbitrage opportunities using binary signalling. Different coding schemes are strategies that trade off between reliability and latency. When one trader has a better channel, the second trader should not compete. With statistically identical channels, we find there are two different regimes of channel noise for which: there is a unique Nash equilibrium yielding ties; and there are two Nash equilibria with different winners.
Image Segmentation and Restoration Using Parametric Contours With Free Endpoints
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we introduce a novel approach for active contours with free endpoints. A scheme is presented for image segmentation and restoration based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.
Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation
Venue: arXiv cs.NE 2015
Year: 2015

A control theoretic approach is presented in this paper for both batch and instantaneous updates of weights in feed-forward neural networks. The popular Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal weight update law. The remarkable contribution in this paper is that closed form solutions for both optimal cost and weight update can be achieved for any feed-forward network using HJB equation in a simple yet elegant manner. The proposed approach has been compared with some of the existing best performing learning algorithms. It is found as expected that the proposed approach is faster in convergence in terms of computational time. Some of the benchmark test data such as 8-bit parity, breast cancer and credit approval, as well as 2D Gabor function have been used to validate our claims. The paper also discusses issues related to global optimization. The limitations of popular deterministic weight update laws are critiqued and the possibility of global optimization using HJB formulation is discussed. It is hoped that the proposed algorithm will bring in a lot of interest in researchers working in developing fast learning algorithms and global optimization.
Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation Scheme
Venue: arXiv cs.NE 2015
Year: 2015

In this paper a multi-parameter A*(A- star)-ants based algorithm is proposed in order to find the best optimized multi-parameter path between two desired points in regions. This algorithm recognizes paths, according to user desired parameters using electronic maps. The proposed algorithm is a combination of A* and ants algorithm in which the proposed A* algorithm is the prologue to the suggested ant based algorithm .In fact, this A* algorithm invigorates some paths pheromones in ants algorithm. As one of implementations of this method, this algorithm was applied on a part of Kerman city, Iran as a multi-parameter vehicle navigator. It finds the best optimized multi-parameter direction between two desired junctions based on city traveler parameters. Comparison results between the proposed method and ants algorithm demonstrates efficiency and lower cost function results of the proposed method versus ants algorithm.
Time-Frequency Trade-offs for Audio Source Separation with Binary Masks
Venue: arXiv cs.SD 2015
Year: 2015

The short-time Fourier transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameterizes the trade-off between time and frequency resolution. However, it is not yet known how this parameter affects the operation of the binary mask in terms of separation quality for real-world signals such as speech or music. Here, we demonstrate that the trade-off between time and frequency in the STFT, used to perform ideal binary mask separation, depends upon the types of source that are to be separated. In particular, we demonstrate that different window sizes are optimal for separating different combinations of speech and musical signals. Our findings have broad implications for machine audition and machine learning in general.
Estimating Attendance From Cellular Network Data
Venue: arXiv cs.SI 2015
Year: 2015

We present a methodology to estimate the number of attendees to events happening in the city from cellular network data. In this work we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated to the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15% of the actual number of attendees.
Lexical Translation Model Using a Deep Neural Network Architecture
Venue: arXiv cs.CL 2015
Year: 2015

In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.
On Maximal Unbordered Factors
Venue: arXiv cs.DS 2015
Year: 2015

Given a string $S$ of length $n$, its maximal unbordered factor is the longest factor which does not have a border. In this work we investigate the relationship between $n$ and the length of the maximal unbordered factor of $S$. We prove that for the alphabet of size $σ\ge 5$ the expected length of the maximal unbordered factor of a string of length~$n$ is at least $0.99 n$ (for sufficiently large values of $n$). As an application of this result, we propose a new algorithm for computing the maximal unbordered factor of a string.
Application of neural networks to identify trolls in social networks
Venue: arXiv cs.SI 2015
Year: 2015

In this paper we developed and tested a new algorithm of detecting in social networks users (so-called trolls) who behave in an insulting and provocative way towards other users. In order to detect trolls it is proposed to unite users in groups where all the members have a similar way of communicating. Defining the number of group and distributing the users into these groups is carried out automatically due to application of neural networks of special type - Kohonens self-organized maps. As for users characteristics according to which the distribution into groups might be done we suggest using such data as the number of comments, the average comment length and indicators determining the emotional state of the user (the frequency of encountering certain characters in comments).
Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey
Venue: arXiv cs.CV 2015
Year: 2015

Computer Vision, either alone or combined with other technologies such as radar or Lidar, is one of the key technologies used in Advanced Driver Assistance Systems (ADAS). Its role understanding and analysing the driving scene is of great importance as it can be noted by the number of ADAS applications that use this technology. However, porting a vision algorithm to an embedded automotive system is still very challenging, as there must be a trade-off between several design requisites. Furthermore, there is not a standard implementation platform, so different alternatives have been proposed by both the scientific community and the industry. This paper aims to review the requisites and the different embedded implementation platforms that can be used for Computer Vision-based ADAS, with a critical analysis and an outlook to future trends.
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural networks. The proposed technique reduces significantly the computational complexity, which in turn, allows us to train layers with a large number of kernels with out the associated time penalty. As a consequence we are able to train convolutional neural network on a very large number of output classes with only a small increase in the computational cost. To show the effectiveness of the technique we train a new output layer on a pretrained network using both the regular multiplicative approach and our proposed hashing methodology. Our results showed no drop in performance and demonstrate, with our implementation, a 7 fold speed up during the training.
Computation of thermal properties via 3D homogenization of multiphase materials using FFT-based accelerated scheme
Venue: arXiv cs.CE 2015
Year: 2015

In this paper we study the thermal effective behaviour for 3D multiphase composite material consisting of three isotropic phases which are the matrix, the inclusions and the coating media. For this purpose we use an accelerated FFT-based scheme initially proposed in Eyre and Milton (1999) to evaluate the thermal conductivity tensor. Matrix and spherical inclusions media are polymers with similar properties whereas the coating medium is metallic hence better conducting. Thus, the contrast between the coating and the others media is very large. For our study, we use RVEs (Representative volume elements) generated by RSA (Random Sequential Adsorption) method developed in our previous works, then, we compute effective thermal properties using an FFT-based homogenization technique validated by comparison with the direct finite elements method. We study the thermal behaviour of the 3D-multiphase composite material and we show what features should be taken into account to make the computational approach efficient.
The xSAP Safety Analysis Platform
Venue: arXiv cs.SE 2015
Year: 2015

This paper describes the xSAP safety analysis platform. xSAP provides several model-based safety analysis features for finite- and infinite-state synchronous transition systems. In particular, it supports library-based definition of fault modes, an automatic model extension facility, generation of safety analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation of Fault Trees, failure propagation analysis using Timed Failure Propagation Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several industrial projects as verification back-end, and is currently being evaluated in a joint R&D Project involving FBK and The Boeing Company.
Grouping Based Blind Interference Alignment for $K$-user MISO Interference Channels
Venue: arXiv cs.IT 2015
Year: 2015

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for $K$-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems
Venue: arXiv cs.LG 2015
Year: 2015

We analyze the problem of using Explore-Exploit techniques to improve precision in multi-result ranking systems such as web search, query autocompletion and news recommendation. Adopting an exploration policy directly online, without understanding its impact on the production system, may have unwanted consequences - the system may sustain large losses, create user dissatisfaction, or collect exploration data which does not help improve ranking quality. An offline framework is thus necessary to let us decide what policy and how we should apply in a production environment to ensure positive outcome. Here, we describe such an offline framework. Using the framework, we study a popular exploration policy - Thompson sampling. We show that there are different ways of implementing it in multi-result ranking systems, each having different semantic interpretation and leading to different results in terms of sustained click-through-rate (CTR) loss and expected model improvement. In particular, we demonstrate that Thompson sampling can act as an online learner optimizing CTR, which in some cases can lead to an interesting outcome: lift in CTR during exploration. The observation is important for production systems as it suggests that one can get both valuable exploration data to improve ranking performance on the long run, and at the same time increase CTR while exploration lasts.